Adaptive knowledge propagation in Web ontologies

We focus on the problem of predicting missing assertions in Web ontologies. We start from the assumption that individual resources that are similar in some aspects are more likely to be linked by specific relations: this phenomenon is also referred to as homophily and emerges in a variety of relational domains. In this article, we propose a method for (1) identifying which relations in the ontology are more likely to link similar individuals and (2) efficiently propagating knowledge across chains of similar individuals. By enforcing sparsity in the model parameters, the proposed method is able to select only the most relevant relations for a given prediction task. Our experimental evaluation demonstrates the effectiveness of the proposed method in comparison to state-of-the-art methods from the literature.